4K Video Frame Interpolation Based on Combination of Optical Flow Estimation and Kernel Estimation
At present,most video frame interpolation technologies have achieved good results,but due to the lack of 4K video data sets,these methods are not ideal for ultra-high-definition video processing.In order to solve the above prob-lems,this paper creates an ultra-high-definition video dataset UHD4K120FPS.At the same time,for 4K video,this paper proposes a video frame interpolation model based on kernel estimation and optical flow estimation.Specifically,the two in-put frames are respectively input into the kernel estimation sub-network and the optical flow estimation sub-network to ex-tract the features of kernel estimation and optical flow estimation,and the extracted features are processed and input into the post-processing fusion sub-network,and warped by cubic convolution and multiple convolutions output the final result.In this paper,training and verification tests are carried out on different data sets.
video frame interpolationdeep learningoptical flow4K video
万方数据
维普
